Other models [1]}, [2]}, [3]} adjusted the bounding boxes.
The intuition was to do object detection first and then refine the instances' contours.
Mask R-CNN [1]} used a feature pyramid network [5]} to extract a feature hierarchy in-network and an FCN to get a segmentation mask in each region of interest.
This region-based approach had proper segmentation but depended on the accurate detection of objects (bounding box).
Hourglass models, on the other hand, do not have this constraint.
Other models [6]} require more delimited boundaries for the segmentation as masks instead of just a box.
They also use sliding operations to obtain better adjustment [7]} to the final targets.
